1. Field of the Invention
The present invention relates generally to an apparatus and method for partial adaptive transmission in a Multiple-Input Multiple-Output (MIMO) system. More particularly, the present invention relates to an apparatus and method for partial adaptive transmission for transmitting data by using a dominant eigen dimension of a correlation matrix in a MIMO system having a spatial correlation between channels.
2. Description of the Related Art
Channel information is important in a Multiple-Input Multiple-Output (MIMO) system to achieve a high system capacity. When a transmitting end knows the MIMO channel information, high system capacity can be achieved by using a Singular Value Decomposition (SVD) scheme and a water filling scheme. In a time-varying channel environment, the MIMO channel information varies over time, and thus the information needs to be fed back periodically from a receiving end to the transmitting end. However, this feed back of information leads to an increase in overhead. To reduce the feedback information overhead, a quantization method may be used. However, the quantization method suffers in that quantization noise increases in proportion to a dimension of a MIMO channel, thereby decreasing performance.
A full adaptive transmission method will now be described as an example of a conventional adaptive MIMO transmission method. In the following description, a system model includes a transmitting end which has nt antennas and a receiving end which has nr antennas. In this case, a Receive (Rx) signal can be expressed by Equation (1) below.y=Hx+n  (1)
In Equation (1), H denotes an (nr×nt)-dimensional channel matrix in which an average of each element is 0 and a dispersion of each element is 1, x denotes an (nt×1)-dimensional Transmit (Tx) signal vector having a power constraint of P, and n denotes an (nr×1)-dimensional Additive White Gaussian Noise (AWGN) vector in which a dispersion of each element is 1.
A correlation matrix is defined by Equation (2) below.
                              R          t                :=                              E            ⁢                          {                                                H                  *                                ⁢                H                            }                                            n            r                                              (        2        )            
In Equation (2), * denotes a conjugate transpose operation. The correlation matrix can be SVD-decomposed as expressed by Equation (3) below.Rt=QΣ2Q*  (3)
In Equation (3), Q denotes an (nt×nt)-dimensional unitary matrix, where Q=[q1 . . . qnt], and Σ denotes an (nt×nt)-dimensional diagonal matrix having diagonal elements of σ1≧ . . . ≧σnt, where Σ2 diag{σ12, . . . , σnt2}. Herein, the channel matrix can be expressed by Equation (4) below by using the correlation matrix.H={tilde over (H)}Rt1/2  (4)
In Equation (4), {tilde over (H)} denotes an (nr×nt)-dimensional matrix and satisfies Equation (5) below.
                              E          ⁡                      [                                                            h                  ~                                i                *                            ⁢                                                h                  ~                                j                                      ]                          =                  {                                                                      0                  ,                                                                              i                  ≠                  j                                                                                                                          n                    r                                    ,                                                                              i                  =                  j                                                                                        (        5        )            
In Equation (5), {tilde over (h)}i denotes an ith column of {tilde over (H)}.
The capacity of a MIMO system using the full adaptive transmission method can be expressed by Equation (6) below.C=log2det(I+HKxH*)  (6)
In Equation (6), the capacity can be maximized by optimizing a Tx covariance matrix Kx, where Kx:=E{xx*}. The optimal covariance matrix is related to an instantaneous channel matrix which is decomposed as expressed by Equation (7) below.H=UΛV*  (7)
In Equation (7), U and V respectively denote an (nr×nr)-dimensional unitary matrix and an (nt×nt)-dimensional unitary matrix, where U=[u1 . . . un] and V=[v1 . . . vnt], and Λ denotes an (nr×nt)-dimensional diagonal matrix in which diagonal elements λ1≧ . . . ≧λnmin are greater than 0 and the remaining elements are 0, where nmin=min(nt,nr). In this case, an optimal Kx can be expressed by Equation (8) below.Kx=VPV*  (8)
In Equation (8), P=diag{P1, . . . , Pnt} denotes an optimal Tx power which maximizes the capacity, and is an (nt×nt)-dimensional diagonal matrix in which first nmin diagonal elements are obtained by using the water filling scheme with respect to {λi, i=1, . . . , nmin}, and the remaining diagonal elements are 0. Therefore, the transmitting end requires Tx power allocation information {Pi, i=1, . . . , nmin} and an (nt×nmin)-dimensional precoding matrix Vmin=[v1 . . . vnmin] corresponding to the Tx power allocation information. Herein, the system capacity of Equation (6) can be expressed by Equation (9) below.
                              C          full                =                              ∑                          i              =              1                                      n              min                                ⁢                                    log              2                        ⁡                          (                              1                +                                                      λ                    i                    2                                    ⁢                                      P                    i                                                              )                                                          (        9        )            
In Equation (9), λi denotes an ith singular value of the channel matrix H, and Pi denotes Tx power allocated to an ith spatial channel.
However, the Tx power allocation information and the precoding matrix, used by the transmitting end to maximize capacity, must be provided as feed back by a receiving end, thus increasing the amount of necessary feedback information and overhead. Accordingly, there is a need for a new adaptive MIMO transmission method capable of reducing the feedback information overhead and maximizing performance.